Rency S. Varghese

Bio: Rency S. Varghese is an academic researcher from Georgetown University Medical Center. The author has contributed to research in topics: Biomarker discovery & Population. The author has an hindex of 21, co-authored 61 publications receiving 1732 citations. Previous affiliations of Rency S. Varghese include University of Maine & University of Washington.

Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma

Abstract: A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. This study describes enrichment of serum by denaturing ultrafiltration, which enables an efficient profiling and identification of peptides up to 5 kDa. We consistently detect several hundred peptide-peaks in MALDI-TOF and SELDI-TOF spectra of enriched serum. The sample preparation is fast and reproducible with an average CV for all 276 peaks in the MALDI-TOF spectrum of 11%. Compared to unenriched serum, the number of peaks in enriched spectra is 4 times higher at an S/N ratio of 5 and 20 times higher at an S/N ratio of 10. To demonstrate utility of the methods, we compared 20 enriched sera of patients with hepatocellular carcinoma (HCC) and 20 age-matched controls using MALDI-TOF. The comparison of 332 peaks at p < 0.001 identified 45 differentially abundant peaks that classified HCC with 90% accuracy in this small pilot study. Direct TOF/TOF sequencing of the most abundant peptide matches with high probability des-Ala-fibrinopeptide A. This study shows that enrichment of the low molecular weight fraction of serum facilitates an efficient discovery of peptides that could serve as biomarkers for detection of HCC as well as other diseases.

Detection of hepatocellular carcinoma using glycomic analysis.

Abstract: Purpose: Hepatocellular carcinoma (HCC) represents an increasing health problem in the United States. Serum α-fetoprotein, the currently used clinical marker, is elevated in only ∼60% of HCC patients; therefore, the identification of additional markers is expected to have significant public health impact. The objective of our study was to quantitatively assess N-glycans originating from serum glycoproteins as alternative markers for the detection of HCC. Experimental Design: We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for quantitative comparison of 83 N-glycans in serum samples of 202 participants (73 HCC cases, 77 age- and gender-matched cancer-free controls, and 52 patients with chronic liver disease). N-glycans were enzymatically released from serum glycoproteins and permethylated before mass spectrometric quantification. Results: The abundance of 57 N-glycans was significantly altered in HCC patients compared with controls. The sensitivity of six individual glycans evaluated for separation of HCC cases from population controls ranged from 73% to 90%, and the specificity ranged from 36% to 91%. A combination of three selected N-glycans was sufficient to classify HCC with 90% sensitivity and 89% specificity in an independent validation set of patients with chronic liver disease. The three N-glycans remained associated with HCC after adjustment for chronic viral infection and other known covariates, whereas the other glycans increased significantly at earlier stages of the progression of chronic viral infection to HCC. Conclusion: A set of three identified N-glycans is sufficient for the detection of HCC with 90% prediction accuracy in a population with high rates of hepatitis C viral infection. Further evaluation of a wider clinical utility of these candidate markers is warranted.

LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort.

Abstract: Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3-5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC-MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

Peak selection from MALDI-TOF mass spectra using ant colony optimization

Abstract: Motivation: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. Results: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. Availability: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm Contact: hwr@georgetown.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

A review of feature selection techniques in bioinformatics

Abstract: Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications. Contact: yvan.saeys@psb.ugent.be Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Abstract: To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

mTOR Signaling in Growth, Metabolism, and Disease

Abstract: The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.

The ATM protein kinase: regulating the cellular response to genotoxic stress, and more

Abstract: The protein kinase ataxia-telangiectasia mutated (ATM) is best known for its role as an apical activator of the DNA damage response in the face of DNA double-strand breaks (DSBs). Following induction of DSBs, ATM mobilizes one of the most extensive signalling networks that responds to specific stimuli and modifies directly or indirectly a broad range of targets. Although most ATM research has focused on this function, evidence suggests that ATM-mediated phosphorylation has a role in the response to other types of genotoxic stress. Moreover, it has become apparent that ATM is active in other cell signalling pathways involved in maintaining cellular homeostasis.